*(Bridges: Precision-induced LMC barriers × Agentic Context Management × Computing Equilibrium Beyond Unilateral Deviation × AiScientist)*
Adversarial Debate Score
48% survival rate under critique
Expert panel critique
Independent views, each critiquing the hypothesis on its own — the score rewards genuine disagreement and discounts consensus.
Supporting Research Papers
- Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
Production AI agents'failures are less often due to an inability to reason well and more often because they cannot manage what is in their reasoning context: conversation histories, large prompts, lar...
- Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models
The advent of agentic multimodal models has empowered systems to actively interact with external environments. However, current agents suffer from a profound meta-cognitive deficit: they struggle to a...
- Toward Autonomous Long-Horizon Engineering for ML Research
Autonomous AI research has advanced rapidly, but long-horizon ML research engineering remains difficult: agents must sustain coherent progress across task comprehension, environment setup, implementat...
- Human-on-the-Bridge: Scalable Evaluation for AI Agents
AI agents must be evaluated as behavioral systems, not as isolated response generators. They reason across turns, call tools, preserve context, follow policies, and act under uncertainty. Existing met...
- Context, Reasoning, and Hierarchy: A Cost-Performance Study of Compound LLM Agent Design in an Adversarial POMDP
Deploying compound LLM agents in adversarial, partially observable sequential environments requires navigating several design dimensions: (1) what the agent sees, (2) how it reasons, and (3) how tasks...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.